Robust Bayesian Inference for Simulator-based models via the MMD Posterior Bootstrap

This repository contains all code needed to recreate the results in the paper "Robust Bayesian Inference for Simulator-based models via the MMD Posterior Bootstrap" by Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas and François-Xavier Briol. The code for the MMD posterior bootstrap is written in Python. Our method uses JAX which is compatible with GPU usage, hence we provide both the files needed to run experiments locally and an .ipynb file compatible for use with Google Colaboratory. We use the R code (ABC_Rcode folder) provided by Bernton, Jacob, Gerbert & Robert (2019) here to compare against ABC with the Wasserstein distance as well as the code by Pacchiardi & Dutta (2021) here to compare against MMD-Bayes with the kernel score (Appendix, Figure 10). A more detailed README file can be found inside the src folder.

Requirements

• Python == 3.7.*
• Numpy == 1.19.5
• Jax == 0.2.13
• SciPy == 1.4.1
• Seaborn == 0.11.2

Reproducing experiments

• The folder data contains all the datasets used for the experiments.
• All scripts have paths to folders named data, results and plots. You can create such folders or set your own paths.
• To reproduce the experiments for the NPL based methods (i.e. NPL-MMD, NPL-WAS and NPL-WLL) locally, run run_gaussian.py, run_gandk.py and run_togswitch.py by setting the relevant data/results/plots paths directly after imports and indicating whether you want to generate new datasets or use the ones used in the paper (located in data folder). The files will run, save and plot the results to the relevant paths. Alternatively, the notebook Experiments_notebook.ipynb is optimised for use with Google colab. The notebook mounts your google drive and calls all the relevant py scripts so you need to import the src folder in your google drive.
• To run the experiments for the same datasets using the Wasserstein-ABC method run the run_wabc_experiments.R file which is entirely based on the code provided by the authors in Bernton, Jacob, Gerbert & Robert (2019).

References

• Bernton, E., Jacob, P.E., Gerber, M. and Robert, C.P., 2019. Approximate Bayesian computation with the Wasserstein distance. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(2), pp.235-269
• Pacchiardi, L. and Dutta, R., 2021. Generalized Bayesian likelihood-free inference using scoring rules estimators. arXiv preprint arXiv:2104.03889